Using Multiple Methods to Reduce Errors in Survey Estimation: The

Report
Using Multiple Methods to Reduce
Errors in Survey Estimation:
The Case of US Farm Numbers
Jaki McCarthy, Denise Abreu, Mark Apodaca, and Leslee Lohrenz
National Agricultural Statistics Service
US Department of Agriculture
Paper presented at the International Total Survey Error Workshop
Stowe, VT
June 2010
What is the goal of reducing TSE?
• Surveys used to estimate a construct
• Goal of TSE reduction is to more accurately
estimate construct in that survey
• Construct: Number of Farms in the US
• Measured in multiple ways:
– June Agricultural Survey
– Census of Agriculture
The Council of Advisors
• Multiple sources provide advice
• Each is likely biased in some direction
• We assume that collective advice is better
than any single source
Comparisons between “advisors” may
uncover errors in both or either
• In most cases, there is no 100% accurate
source
• Each estimate makes different assumptions,
uses different procedures
Farm Number Estimates
• June Agricultural Survey
(JAS)
– Purpose: direct estimates
of acreage and measures
of sampling coverage
–
–
–
–
–
Area Frame Based
In Person Data Collection
Sample Survey
Voluntary
All non-response is
manually estimated
• Census of Agriculture
(COA)
– Purpose: detailed county
level agricultural data on
all commodities produced
and expenses, income and
operator characteristics
– List Based
– Primarily self administered
mail data collection
– Census
– Mandatory
– Non-response weighting
adjustment
Census Undercoverage and
Misclassification
• Historically, JAS is a benchmark for COA
– Area frame has theoretically complete coverage
– Flagship survey for NASS with personal interviews
• Classification Error Survey uncovered errors in
both JAS and COA identification of farms, but
with most in JAS
What perspective?
• Advisor #1: JAS
– Primary objective is to
produce acreage
estimates, farm numbers
are secondary
• Advisor #2: COA
– Primary objective is to
collect information on
ALL farms
Number of Farms
US Farm Numbers
Year
JAS
Census
So we begin with 2 independent
estimates of farm numbers…..
• To improve JAS estimate, additional follow up
was conducted to estimate number of farms
in subset that were originally estimated or
classified as NOT farms
• Result: additional farms missed (misclassified)
in the JAS
• This can be added to original JAS estimates
Number of Farms
US Farm Numbers
Year
JAS
Census
JAS plus follow-up
ADD another advisor:
another independent estimate of farms
• Assumption that operations reporting
themselves as farms on the 2007 COA,
but not JAS were misclassified in JAS
• Another regression estimate based on
this assumption applied to 2009 JAS
survey data
Number of Farms
US Farm Numbers
Year
JAS
Census
JAS plus follow-up
JAS regression
The Council of Advisors is used
to set the “official” farm number
• Each of the methods is measuring the same
construct
• Each of the methods is independent, has different
emphasis, and has its unique errors
• Objective is not to “fix” an individual survey
estimate or measure its errors
• Objective is to combine all of these estimates to
produce the “best” number:
Reducing Total Construct Error
My questions to you:
• Do you use similar practices?
• How do you combine multiple sources of
information?
• What is the best way to do this?
• How does this fit into the TSE context?

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